Computational

Jason Bohland, Ph.D. (Assistant Professor, Department of Health Sciences, Sargent College (CRC)) focuses on understanding the structural and functional architecture of neural circuits in the human brain, with emphasis on those that support speech and language processes. The laboratory uses an integrative approach that combines computational, informatics, and experimental brain imaging methods. A key area of focus is in linking the molecular level of organization to the neural systems and behavioral levels through the use of spatiotemporal profiles of gene expression coupled with brain imaging results, with the ultimate goal of better understanding genotype to phenotype relationships in neurodevelopmental disorders. Dr. Bohland is additionally involved in a collaborative experimental and informatics effort to comprehensively map the mesoscale connectivity patterns in the adult mouse brain.

Daniel Bullock, Ph.D. (Professor, Department of Cognitive and Neural Systems (CRC)) Interests of the Bullock lab are focused on the use of integrated computational models of local circuits implicated in reinforcement learning, planning of action sequences (including speech), and motivated decision making. Current models focus on forebrain circuits within or linking: laminar frontal cortex, the striatum and other parts of the basal ganglia, and midbrain dopaminergic areas. The long-term goal is construction of quantitative models of sufficient accuracy to predict effects of many pharmacological manipulations on decision-making, voluntary behavior, and skill learning.

Uri Eden, Ph.D. (Assistant Professor, Department of Mathematics (CRC)) focuses on developing mathematical and statistical methods to analyze neural spiking activity. He has worked to integrate methodologies related to model identification, statistical inference, signal processing, and stochastic estimation and control, and to expand these methodologies to incorporate point process observation models, making them more appropriate for modeling the dynamics of neural systems observed through spike train data. His research can be divided into two categories; first, a methodological component, focused on developing a statistical framework for relating neural activity to biological and behavioral signals and developing estimation algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to any neural spiking system; second, an application component, wherein these methods are applied to spiking observations in real neural systems to dynamically model the spiking properties of individual neurons, to characterize how ensembles maintain representations of associated biological and behavioral signals, and to reconstruct these signals in real time.

Timothy Gardner, Ph.D. (Assistant Professor, Department of Biology (CRC)) studies how neural circuits form in the development of animal behavior. We focus on vocal learning in songbirds — a subject that lends itself to quantitative approaches. How do songbirds memorize the songs of other birds, and how do these memories influence their own vocal learning? Many songbirds sing fairly normally when reared in isolation, but in the right circumstances, they may also imitate external models. Song learning is therefore the result of innate programming that provides a basic outline for song, and an auditory-memory based learning that builds on the innate program. The laboratory is currently investigating the process that builds and maintains the core sequence of the song behavior. What growth mechanisms form the core structure of song and what are the geometric properties of the resulting circuit? What features of the circuit govern the flexible ordering of song? What homeostatic mechanisms maintain the circuit, and what is the role of spontaneous neural activity in sleep? For genetically identical birds, how would song learning differ? The lab is addressing one or more of these questions through tools including quantitative behavioral experiments and inbreeding, in-vivo imaging, electrophysiology and functional perturbation of neural activity.

Stephen Grossberg, Ph.D. (Professor, Department of Cognitive Neural Systems (CRC)) develops brain models of vision and visual object recognition; audition, speech, and language; development; attentive learning and memory; cognitive information processing; reinforcement learning and motivation; cognitive-emotional interactions; navigation; sensory-motor control and robotics; and mental disorders. These models involve many parts of the brain, ranging from perception to action, and multiple levels of brain organization, ranging from individual spikes and their synchronization to cognition. He also collaborates in experimental projects to test predictions of his models, carrys out analyses of the mathematical dynamics of neural systems, and transfers biological neural models to applications in neuromorphic engineering and technology.

Frank Guenther, Ph.D. (Professor, Departments of Speech, Language, and Hearing Sciences and Cognitive and Neural Systems (CRC)) combines theoretical modeling with behavioral and neuroimaging experiments to characterize the neural computations underlying speech and language. He is also involved in the development of speech prostheses that utilize brain-computer interfaces to restore synthetic speech to paralyzed individuals. The primary goal of the lab is to develop, test, and refine a computational modeling framework that addresses the neural processes underlying speech. This theoretical framework is applied to communication disorders and the design of neural prosthetics in collaborative projects with other labs within and outside of Boston University.

Xue Han, Ph.D. (Assistant Professor, Departments of Biomedical Engineering, Pharmacology and Experimental Therapeutics (CRC/BUMC)) Brain disorders represent the biggest unmet medical need, with many disorders being untreatable, and most treatments presenting serious side effects. The Han laboratory is discovering design principles for novel neuromodulation therapies. They invent and apply a variety of genetic, molecular, pharmacological, optical, and electrical tools to correct neural circuits that go awry within the brain. As an example, they have pioneered several technologies for silencing specific cells in the brain using pulses of light. They have also recently participated in the first pre-clinical testing of a novel neurotechnology, optical neural modulation. Using these novel neurotechnologies and classical ones such as deep brain stimulation (DBS), they modulate the function of neural circuits to establish causal links between neural dynamics and behavioral phenomena (e.g., movement, attention, memory, and decision making). One of their current interests is the investigation of how neural synchrony arises within and across brain regions, and how synchronous activity contributes to normal cognition and pathology.

Michael Hasselmo, Ph.D. (Professor, Department of Psychology (CRC)) Research in the Hasselmo laboratory is concerned with the cortical dynamics of memory-guided behavior, including effects of neuromodulatory receptors and the role of theta rhythm oscillations in cortical function. Neurophysiological techniques are used to analyze intrinsic and synaptic properties of cortical circuits in the rat, and to explore the effects of modulators on these properties. Computational modeling is used to link this physiological data to memory-guided behavior. Experiments using multiple single-unit recording in behavioral tasks are designed to test predictions of the computational models. Areas of focused research include episodic memory function and theta rhythm dynamics in the entorhinal cortex, prefrontal cortex and hippocampal formation. Research addresses physiological effects relevant to Alzheimer’s disease, schizophrenia and depression.

Nancy Kopell, Ph.D. (Professor, Member of the GPN GEC & Computational Neuroscience Curriculum Committee, Department of Mathematics (CRC), National Academy of Sciences) is interested in the dynamics of cortical electrical activity associated with sensory processing, cognition and motor control. This broad area includes 1) the physiological and anatomical bases of the multiple rhythms measured in EEG, MEG and invasive paradigms; 2) the relationships between those rhythms and cognitive function; 3) how pathologies in those rhythms relate to cognitive and motor symptoms in neurological diseases. Dr. Kopell is the currently co-director of the Center for BioDynamics (CBD) in the College of Engineering at Boston University. This multidisciplinary, interdepartmental center aims to train undergraduates, graduates, and postdoctoral fellows in leading techniques from dynamical systems theory and its applications to biology and engineering.

Mark Kramer, Ph.D. (Assistant Professor, Department of Mathematics (CRC)) focuses his research on topics in mathematical neuroscience, with particular emphasis on neural rhythms, dynamical systems, data analysis, and brain disease. The work is highly interdisciplinary, involving mathematicians, statisticians, neuroscientists and clinical collaborators. He is especially interested in the quantitative analysis of electrophysiological data, and understanding the biophysical mechanisms that produce the dynamic neural activity observed. His current clinical work focuses on analyzing multivariate data and identifying the mechanisms that support the pathological brain activity characteristic of epilepsy.

Jason Ritt, Ph.D. (Assistant Professor, Biomedical Engineering) Dr. Ritt’s research concentrates on how organisms gather and use information from their environment, through processes of active sensing and sensory decision making. Current projects employ electrophysiological, behavioral, optogenetic and theoretical methods applied to the rodent whisker system, a highly refined tactile sensory system. Experiments combine multi-electrode recording of brain activity; high speed videography of behavior and development of automated image analysis algorithms; and optical stimulation of specific cell types (e.g., excitatory vs. inhibitory neurons) using genetically targeted expression of light sensitive ion channels. Parallel modeling uses tools from dynamical systems, control theory and decision theory. Augmenting experiments with model-driven, real-time feedback forms a basis for development of brain machine interfaces, with an emphasis on sensory neural prosthetics, in addition to providing state of the art tools to address basic questions of neural function.

Michele Rucci, Ph.D. (Associate Professor, Departments of Psychology and Biomedical Engineering (CRC)) directs The Active Perception Lab that focuses on active perception in biological and artificial systems. Experimental and theoretical approaches are combined to examine motor influences on perceptual performance and on the encoding of sensory information in the brain. Robots replicating the sensory-motor strategies of various species are studied in an effort to develop efficient machine perception systems. Research in the Active Perception Laboratory has raised specific hypotheses regarding the influences of eye movements during visual development and in the neural encoding of visual information. This research has also demonstrated the involvement of fixational eye movements in fine spatial vision, produced a new system for experimental studies of visual neuroscience, and led to the development of robots directly controlled by models of the brain.

Barbara Shinn-Cunningham, Ph.D. (Professor, Member of the GPN GEC, Department of Biomedical Engineering (CRC)) Shinn-Cunningham is the Director of the Auditory Neuroscience Laboratory in the Boston University Hearing Research Center, housed in the Department of Cognitive and Neural Systems. Projects in the Auditory Neuroscience Laboratory explore how we perceive sound sources in ordinary listening environments that contain multiple, competing sources, echoes, and reverberation. They are investigating how auditory attention and the perceptual organization of sound influences perception, how the brain encodes features of sound important for perception (including spatial auditory cues), the role of multimodal interactions in perception, and development of physiologically based computational models of auditory processing. A variety of methods are employed, including psychophysics, modeling, EEG, acoustical measurement, and single-unit recording.

David Somers, Ph.D. (Professor, Academic Director, Graduate Program for Neuroscience/Computational Specialization,Department of Psychology (CRC), heads the Perceptual Neuroimaging Laboratory that investigates the neural and cognitive representations and mechanisms of perception, attention, and perceptual short-term memory in humans using functional magnetic resonance imaging, computational modeling and psychophysics. In addition to the primary focus on vision, collaborative work investigates tactile and auditory processing. Functional MRI studies focus on within-subject data analysis that permits the functional segregation of small cerebral cortical areas that are often obscured in across-subject analysis. Recently, they identified and characterized two new visuotopic areas in human posterior parietal cortex using these methods.

Lucia Vaina, M.D., Ph.D. (Professor, Department of Biomedical Engineering (CRC)) The adult brain constantly adapts to changes in stimuli, and this plasticity is manifest not only as learning and memory but also as dynamic changes in information transmission and processing. The goal of research in the Brain and Vision Research Laboratory is to understand the mechanisms mediating human visual perception in healthy and damaged human brain, long-term plasticity and short-term dynamics in networks of the adult normal and damaged (from stroke) cortex by using interactively multimodal imaging (fMRI, MEG, DTI), psychophysics, and biologically constrained computational modeling. An additional facet of our research is translational, conducted hand in hand with several neurologists and psychiatrist clinicians, that investigates multisensory processing for facilitating behavior and recovery in stroke patients.